What’s the difference between Rover and Overmind?
James Lane
June 27, 2025
Making sense of complex infrastructure changes before they hit production has become one of the biggest challenges facing engineering teams today. When a simple Terraform plan can span hundreds of resources across multiple services, understanding what's actually going to happen and more importantly, what could go wrong requires more than just reading through text output.
When using Terraform the output from terraform plan can be used to help understand the impact your infrastructure changes. Terraform has terraform graph to help visualise these changes however when dealing with large or complex infrastructures this can quickly become difficult to navigate. Because of this several visualisation tools have emerged both open source and enterprise.
This comparison examines two fundamentally different approaches to solving this problem: Rover's visual representation philosophy and Overmind's predictive intelligence approach.
Rover takes the traditional route of making complex data more digestible through visualization. As an open-source tool, it transforms Terraform plan output into interactive graphical representations that help teams understand their infrastructure changes at a glance.
The tool works by parsing both your Terraform plan and configuration files to generate three core visualization components:
**Resource Overview** provides a high-level summary of what resources will be created, modified, or destroyed. This gives teams an immediate sense of the scope and nature of their changes.
**Resource Map** creates a navigable interface where users can explore resources and their relationships. Think of it as a bird's-eye view of your infrastructure that you can zoom into for details.
**Resource Graph** focuses on dependencies and connections between resources, helping identify potential ripple effects from changes.
The process is straightforward: generate your Terraform plan with JSON output, feed it to Rover along with your configuration files, and explore your changes through the interactive interface.
What makes Rover particularly useful is its combination of plan and configuration data. While many tools only show you what's changing, Rover also shows you the broader context of your infrastructure configuration. This comprehensive view helps teams spot potential issues that might not be obvious from the plan output alone.
Rover does this by:
The quickest way to get up and running with Rover is by using Docker.
Run the following command in any Terraform workspace to generate a visualisation. This command copies all the files in your current directory to the Rover container and exposes port :9000
Once Rover is running on localhost:9000, navigate to it in a browser to find the visualisation.
From here you can then explore the visualisation of your terraform plan output. The legend on the left hand side shows you the different categorisations of resources and other items. With rover you are able to export the generated graph as .SVG file.
Overmind takes a fundamentally different approach. Instead of just showing you what will change, it analyzes whether those changes are safe to deploy. This shift from visualisation to intelligence represents a different philosophy about what teams actually need when making deployment decisions.
When you run `overmind terraform plan`, the tool doesn't just parse your plan file, it actively queries your live infrastructure through APIs to understand the current state of your systems. This real-time analysis reveals dependencies and connections that exist outside your Terraform state, including resources created through the AWS console, CloudFormation, or other tools.
The key difference lies in what happens next. While visualisation tools stop at showing you the structure of your changes, Overmind evaluates the risk associated with those changes. It considers factors like:
- Hidden dependencies that don't appear in your Terraform state
- Historical patterns from previous deployments
- Timing considerations (deploying during peak hours vs. maintenance windows)
- Cross-service relationships that could amplify the impact of failures
This analysis results in actionable risk assessments rather than just visual representations. Instead of asking "what will change?", teams can answer "is it safe to change this now?"
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Next you will need to create a change. This can be done by:
Let's look at an example using the Github action. Based on the planned changes and the relationships that we have discovered, Overmind discovers the blast radius of what might be affected by this change including resources not in terraform.
The Github action can be set up as part of you CI/CD pipeline to notify you of the affected items/ apps of your change automatically. However any CI /CD tool can be supported using the CLI.
You will then receive a set of human-readable risks that you are able to see if there is anything unexpected or of concern. If you notice that the change might affect more than you thought, you can modify either your code, or the way you plan to roll out and monitor the change to account for it. These risks can either be commented back as part of your CI / CD pipeline or viewed in the app.
By understanding which services would potentially by affected by the planned change, you'll have a full picture and will be able to fix any issues before it effects production.
When you're ready to start the change, Overmind will take a snapshot before and after to validate that the change went through as intended. Allowing you to view the health status of resources and config diffs in detail within the app.
Before jumping into a comparison of the two it is worth providing some context to the application we are going to be making changes to. It is Kubernetes cluster that manages some API gateways using AWS services such as EKS, EFS & Route53.
Let’s take a look at what this change would look like within Rover. By taking that same Terraform plan output and running it through Rover we get the below.
Within Overmind, by selecting the resource that we will be changing, we get the following blast radius. We are able to see the items related to this resource that we are changing, meaning that we are informed on the impact of this change.
The distinction between these approaches becomes most apparent when dealing with infrastructure that changes outside of Terraform. Many organisations have resources created through multiple tools, manual console changes, or legacy systems that aren't fully captured in their Terraform state.
Rover can only visualise what's in your Terraform configuration and plan. If you have hidden dependencies or resources created outside Terraform, they won't appear in the visualization. This limitation can create blind spots where changes appear simple in the visualization but are actually risky due to undocumented dependencies.
Overmind's real-time infrastructure querying addresses this gap. By actively scanning your current infrastructure state, it can identify dependencies and relationships that don't exist in your Terraform files. This capability becomes increasingly valuable as organisations deal with:
- Legacy infrastructure that predates infrastructure-as-code adoption
- Resources created through emergency changes or manual interventions
- Multi-tool environments where Terraform coexists with CloudFormation, Pulumi, or other tools
- Dynamic environments where resources are created and destroyed by application logic
The decision between these approaches often comes down to what problem you're trying to solve and how your team currently handles infrastructure changes.
Choose Rover when your primary challenge is complexity and communication. If your team has trouble understanding and discussing complex Terraform changes, if you need to onboard new team members to existing infrastructure, or if you want to improve collaboration around infrastructure changes, visualisation provides clear value.
Choose Overmind when your primary challenge is safety and risk management. If your team has experienced outages from seemingly simple changes, if you have infrastructure created outside Terraform, or if you want to enable faster deployment by increasing confidence in change safety, predictive intelligence becomes more valuable.
Both tools are freely available: